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Sunday, April 15, 2018

Machine Learning and Signal Processing

Quite interesting, linking classical signal processing and machine learning.   Signal processing was part of classical engineering training.   I assume this is still common.  Ultimately its all about signals that we are trying to detect, but here the signals contain random (Stochastic) noise and order we can extract.  This piece has coding examples, and is Technical, but straightforward in approach.   Worth at least filing away for problems of this type.

Machine Learning with Signal Processing Techniques By Ahmet Taspinar  Blog


Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals.

Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals.

Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them.

In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals.

At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a person by their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc.

In this blog-post we’ll discuss the following topics:

Basics of Signals
Transformations between time- and frequency-domain by means of FFT, PSD and autocorrelation.
Statistical parameter estimation and feature extraction
Example dataset: Classification of human activity
Extracting features from all signals in the training and test set
Classification with (traditional) Scikit-learn classifiers
Finals words  .... "

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